Date of Award

Spring 5-31-2021

Degree Type

Thesis

Degree Name

M.S.

Degree Program

Computer Science

Department

Computer Science

Major Professor

Dr. Mahdi Abdelguerfi

Second Advisor

Dr. Tamjid Hoque

Third Advisor

Dr. Elias Ioup

Fourth Advisor

Dr. Shaikh Arifuzzaman

Abstract

Terminal Procedure Charts are a constantly updated and necessary tool for aircraft personnel to approach and take off from airport runways safely. Detecting changes within these charts is a time-consuming and laborious process. Here machine learning techniques were used to predict regions of change in charts based on detecting the charts image regions and comparing features extracted from those regions. Outlined are methodologies to detect differences between two separate charts to produce images with changed regions clearly indicated. Both more conventional computer vision and machine learning techniques were applied. For images with minor shifts, the proposed model is able to ignore them to a greater degree than the baseline.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

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